biostudies-literatureUnknown27Arbel-Groissman N<h4>Background</h4>Disappointing medical care (DMC) encompasses cases of medical failures, malpractice, or errors. Literature suggests that individuals' perceptions of harm resulting from medical procedures influence their intention to seek legal recourse and that apologies may mitigate the inclination for legal action.<h4>Objective</h4>Here, we aim to scrutinize and potentially challenge this prevailing notion.<h4>Methods</h4>We conducted 4 studies using a dataset of social media posts detailing possible DMC incidents to which we linked a proxy for legal action, specifically, future posts related to legal action. Study 1 used a machine learning model to predict a proxy for an intent to file a lawsuit based on the content of 3815 posts. Two preregistered crowdsourcing studies (N=1115) asseJournal of medical Internet researche77493https://www.ebi.ac.uk/biostudies/studies/S-EPMC12569484biostudies-literatureOn the Influence of Apologies on the Likelihood of Lawsuits in Cases of Perceived Medical Negligence: Analysis of Archival and Experimental Data.PMC12569484Dorfman EYom-Tov ERafaeli AArbel-Groissman NFeigin PDfalseOn the Influence of Apologies on the Likelihood of Lawsuits in Cases of Perceived Medical Negligence: Analysis of Archival and Experimental Data.<h4>Background</h4>Disappointing medical care (DMC) encompasses cases of medical failures, malpractice, or errors. Literature suggests that individuals' perceptions of harm resulting from medical procedures influence their intention to seek legal recourse and that apologies may mitigate the inclination for legal action.<h4>Objective</h4>Here, we aim to scrutinize and potentially challenge this prevailing notion.<h4>Methods</h4>We conducted 4 studies using a dataset of social media posts detailing possible DMC incidents to which we linked a proxy for legal action, specifically, future posts related to legal action. Study 1 used a machine learning model to predict a proxy for an intent to file a lawsuit based on the content of 3815 posts. Two preregistered crowdsourcing studies (N=1115) asse2025-01-01T00:00:00Z2025 Oct2026-06-05T07:10:21.821Z2026-05-14T03:08:55.529ZS-EPMC125694844108600210.2196/77493